Subtype-WGME enables whole-genome-wide, multi-omics cancer subtyping
2024
East China University of Science and Technology, Shanghai, China
In this study, an innovative strategy for integrating whole-genome-wide multi-omics data is presented, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that the proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, a robust pipeline for identifying whole-genome-wide biomarkers was established, unearthing 195 significant biomarkers. Furthermore, an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping was conducted. The investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, the findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.
Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping
Zhe Wang
Added on: 07-08-2024
[1] https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(24)00125-5
